|
|
--- |
|
|
base_model: BAAI/bge-base-en-v1.5 |
|
|
library_name: sentence-transformers |
|
|
pipeline_tag: sentence-similarity |
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|
tags: |
|
|
- sentence-transformers |
|
|
- sentence-similarity |
|
|
- feature-extraction |
|
|
- generated_from_trainer |
|
|
- dataset_size:48 |
|
|
- loss:MultipleNegativesRankingLoss |
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|
widget: |
|
|
- source_sentence: 'Fundamentals of Deep Learning for Multi GPUs. Find out how to |
|
|
use multiple GPUs to train neural networks and effectively parallelize\ntraining |
|
|
of deep neural networks using TensorFlow.. tags: multiple GPUs, neural networks, |
|
|
TensorFlow, parallelize. Languages: Course language: Python. Prerequisites: No |
|
|
prerequisite course required. Target audience: Professionals want to train deep |
|
|
neural networks on multi-GPU technology to shorten\nthe training time required |
|
|
for data-intensive applications.' |
|
|
sentences: |
|
|
- 'Course Name:Hypothesis Testing in Python|Course Description:In this course, learners |
|
|
with foundational knowledge of statistical concepts will dive deeper into hypothesis |
|
|
testing by focusing on three standard tests of statistical significance: t-tests, |
|
|
F-tests, and chi-squared tests. Covering topics such as t-value, t-distribution, |
|
|
chi-square distribution, F-statistic, and F-distribution, this course will familiarize |
|
|
learners with techniques that will enable them to assess normality of data and |
|
|
goodness-of-fit and to compare observed and expected frequencies objectively.|Tags:f-distribution, |
|
|
chi-square distribution, f-statistic, t-distribution, t-value|Course language: |
|
|
Python|Target Audience:Professionals some Python experience who would like to |
|
|
expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite |
|
|
course required: Foundations of Statistics in Python' |
|
|
- 'Course Name:Foundations of Data & AI Literacy for Managers|Course Description:Designed |
|
|
for managers leading teams and projects, this course empowers individuals to build |
|
|
data-driven organizations and integrate AI tools into daily operations. Learners |
|
|
will gain a foundational understanding of data and AI concepts and learn how to |
|
|
leverage them for actionable business insights. Managers will develop the skills |
|
|
to increase collaboration with technical experts and make informed decisions about |
|
|
analysis methods, ensuring their enterprise thrives in today’s data-driven landscape.|Tags:Designed, |
|
|
managers, leading, teams, projects,, course, empowers, individuals, build, data-driven, |
|
|
organizations, integrate, AI, tools, into, daily, operations., Learners, will, |
|
|
gain, foundational, understanding, data, AI, concepts, learn, how, leverage, them, |
|
|
actionable, business, insights., Managers, will, develop, skills, increase, collaboration, |
|
|
technical, experts, make, informed, decisions, about, analysis, methods,, ensuring, |
|
|
their, enterprise, thrives, today’s, data-driven, landscape.|Course language: |
|
|
None|Target Audience:No target audience|No prerequisite course required' |
|
|
- 'Course Name:Fundamentals of Deep Learning for Multi GPUs|Course Description:Find |
|
|
out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining |
|
|
of deep neural networks using TensorFlow.|Tags:multiple GPUs, neural networks, |
|
|
TensorFlow, parallelize|Course language: Python|Target Audience:Professionals |
|
|
want to train deep neural networks on multi-GPU technology to shorten\nthe training |
|
|
time required for data-intensive applications|No prerequisite course required' |
|
|
- source_sentence: 'Data Visualization Design & Storytelling. This course focuses |
|
|
on the fundamentals of data visualization, which helps support data-driven decision-making |
|
|
and to create a data-driven culture.. tags: data driven culture, data analytics, |
|
|
data literacy, data quality, storytelling, data science. Languages: Course language: |
|
|
TBD. Prerequisites: No prerequisite course required. Target audience: Professionals |
|
|
who would like to understand more about how to visualize data, design and concepts |
|
|
of storytelling through data..' |
|
|
sentences: |
|
|
- 'Course Name:Building Transformer-Based NLP Applications (NVIDIA)|Course Description:Learn |
|
|
how to apply and fine-tune a Transformer-based Deep Learning model to Natural |
|
|
Language Processing (NLP) tasks. In this course, you''ll construct a Transformer |
|
|
neural network in PyTorch, Build a named-entity recognition (NER) application |
|
|
with BERT, Deploy the NER application with ONNX and TensorRT to a Triton inference |
|
|
server. Upon completion, you’ll be proficient i.n task-agnostic applications of |
|
|
Transformer-based models. Data Society''s instructors are certified by NVIDIA’s |
|
|
Deep Learning Institute to teach this course.|Tags:named-entity recognition, text, |
|
|
Natural language processing, classification, NLP, NER|Course language: Python|Target |
|
|
Audience:Professionals with basic knowledge of neural networks and want to expand |
|
|
their knowledge in the world of Natural langauge processing|No prerequisite course |
|
|
required' |
|
|
- 'Course Name:Nonlinear Regression in Python|Course Description:In this course, |
|
|
learners will practice implementing a variety of nonlinear regression techniques |
|
|
in Python to model complex relationships beyond simple linear patterns. They will |
|
|
learn to interpret key transformations, including logarithmic (log-log, log-linear) |
|
|
and polynomial models, and identify interaction effects between predictor variables. |
|
|
Through hands-on exercises, they will also develop practical skills in selecting, |
|
|
fitting, and validating the most appropriate nonlinear model for their data.|Tags:nonlinear, |
|
|
regression|Course language: Python|Target Audience:This is an intermediate level |
|
|
course for data scientists who want to learn to understand and estimate relationships |
|
|
between a set of independent variables and a continuous dependent variable.|Prerequisite |
|
|
course required: Multiple Linear Regression' |
|
|
- 'Course Name:Data Visualization Design & Storytelling|Course Description:This |
|
|
course focuses on the fundamentals of data visualization, which helps support |
|
|
data-driven decision-making and to create a data-driven culture.|Tags:data driven |
|
|
culture, data analytics, data literacy, data quality, storytelling, data science|Course |
|
|
language: TBD|Target Audience:Professionals who would like to understand more |
|
|
about how to visualize data, design and concepts of storytelling through data.|No |
|
|
prerequisite course required' |
|
|
- source_sentence: 'Foundations of Probability Theory in Python. This course guides |
|
|
learners through a comprehensive review of advanced statistics topics on probability, |
|
|
such as permutations and combinations, joint probability, conditional probability, |
|
|
and marginal probability. Learners will also become familiar with Bayes’ theorem, |
|
|
a rule that provides a way to calculate the probability of a cause given its outcome. |
|
|
By the end of this course, learners will also be able to assess the likelihood |
|
|
of events being independent to indicate whether further statistical analysis is |
|
|
likely to yield results.. tags: conditional probability, bayes'' theorem. Languages: |
|
|
Course language: Python. Prerequisites: Prerequisite course required: Hypothesis |
|
|
Testing in Python. Target audience: Professionals some Python experience who would |
|
|
like to expand their skill set to more advanced Python visualization techniques |
|
|
and tools..' |
|
|
sentences: |
|
|
- 'Course Name:Foundations of Probability Theory in Python|Course Description:This |
|
|
course guides learners through a comprehensive review of advanced statistics topics |
|
|
on probability, such as permutations and combinations, joint probability, conditional |
|
|
probability, and marginal probability. Learners will also become familiar with |
|
|
Bayes’ theorem, a rule that provides a way to calculate the probability of a cause |
|
|
given its outcome. By the end of this course, learners will also be able to assess |
|
|
the likelihood of events being independent to indicate whether further statistical |
|
|
analysis is likely to yield results.|Tags:conditional probability, bayes'' theorem|Course |
|
|
language: Python|Target Audience:Professionals some Python experience who would |
|
|
like to expand their skill set to more advanced Python visualization techniques |
|
|
and tools.|Prerequisite course required: Hypothesis Testing in Python' |
|
|
- 'Course Name:Foundations of Generative AI|Course Description:Foundations of Generative |
|
|
AI|Tags:Foundations, Generative, AI|Course language: None|Target Audience:No target |
|
|
audience|No prerequisite course required' |
|
|
- 'Course Name:Data Science for Managers|Course Description:This course is designed |
|
|
for managers seeking to bolster their data literacy with a deep dive into data |
|
|
science tools and teams, project life cycles, and methods.|Tags:data driven culture, |
|
|
data analytics, data quality, storytelling, data science|Course language: TBD|Target |
|
|
Audience:This course is targeted for those who would like to understand more about |
|
|
data literacy, make more informed decisions and identify data-driven solutions |
|
|
through data science tools and methods.|No prerequisite course required' |
|
|
--- |
|
|
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
- **Model Type:** Sentence Transformer |
|
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
|
- **Maximum Sequence Length:** 512 tokens |
|
|
- **Output Dimensionality:** 768 tokens |
|
|
- **Similarity Function:** Cosine Similarity |
|
|
<!-- - **Training Dataset:** Unknown --> |
|
|
<!-- - **Language:** Unknown --> |
|
|
<!-- - **License:** Unknown --> |
|
|
|
|
|
### Model Sources |
|
|
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
|
|
### Full Model Architecture |
|
|
|
|
|
``` |
|
|
SentenceTransformer( |
|
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
|
(2): Normalize() |
|
|
) |
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
|
|
First install the Sentence Transformers library: |
|
|
|
|
|
```bash |
|
|
pip install -U sentence-transformers |
|
|
``` |
|
|
|
|
|
Then you can load this model and run inference. |
|
|
```python |
|
|
from sentence_transformers import SentenceTransformer |
|
|
|
|
|
# Download from the 🤗 Hub |
|
|
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v4python") |
|
|
# Run inference |
|
|
sentences = [ |
|
|
"Foundations of Probability Theory in Python. This course guides learners through a comprehensive review of advanced statistics topics on probability, such as permutations and combinations, joint probability, conditional probability, and marginal probability. Learners will also become familiar with Bayes’ theorem, a rule that provides a way to calculate the probability of a cause given its outcome. By the end of this course, learners will also be able to assess the likelihood of events being independent to indicate whether further statistical analysis is likely to yield results.. tags: conditional probability, bayes' theorem. Languages: Course language: Python. Prerequisites: Prerequisite course required: Hypothesis Testing in Python. Target audience: Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools..", |
|
|
"Course Name:Foundations of Probability Theory in Python|Course Description:This course guides learners through a comprehensive review of advanced statistics topics on probability, such as permutations and combinations, joint probability, conditional probability, and marginal probability. Learners will also become familiar with Bayes’ theorem, a rule that provides a way to calculate the probability of a cause given its outcome. By the end of this course, learners will also be able to assess the likelihood of events being independent to indicate whether further statistical analysis is likely to yield results.|Tags:conditional probability, bayes' theorem|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite course required: Hypothesis Testing in Python", |
|
|
'Course Name:Foundations of Generative AI|Course Description:Foundations of Generative AI|Tags:Foundations, Generative, AI|Course language: None|Target Audience:No target audience|No prerequisite course required', |
|
|
] |
|
|
embeddings = model.encode(sentences) |
|
|
print(embeddings.shape) |
|
|
# [3, 768] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(embeddings, embeddings) |
|
|
print(similarities.shape) |
|
|
# [3, 3] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
|
|
--> |
|
|
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|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
|
|
|
* Size: 48 training samples |
|
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
|
* Approximate statistics based on the first 48 samples: |
|
|
| | anchor | positive | |
|
|
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 49 tokens</li><li>mean: 188.12 tokens</li><li>max: 322 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 186.12 tokens</li><li>max: 320 tokens</li></ul> | |
|
|
* Samples: |
|
|
| anchor | positive | |
|
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>Outlier Detection with DBSCAN in Python. Density-Based Spatial Clustering of Applications with Noise, or DBSCAN, contrasts groups of densely-packed data with points isolated in low-density regions. In this course, learners will discuss the optimal data conditions suited to this method of outlier detection. After discussing different basic varieties of anomaly detection, learners will implement DBSCAN to identify likely outliers. They will also use a balancing method called Synthetic Minority Oversampling Technique, or SMOTE, to generate additional examples of outliers and improve the anomaly detection model.. tags: outlier, SMOTE, anomaly, DBSCAN. Languages: Course language: Python. Prerequisites: Prerequisite course required: Intro to Clustering. Target audience: Professionals with some Python experience who would like to expand their skills to learn about various outlier detection techniques.</code> | <code>Course Name:Outlier Detection with DBSCAN in Python|Course Description:Density-Based Spatial Clustering of Applications with Noise, or DBSCAN, contrasts groups of densely-packed data with points isolated in low-density regions. In this course, learners will discuss the optimal data conditions suited to this method of outlier detection. After discussing different basic varieties of anomaly detection, learners will implement DBSCAN to identify likely outliers. They will also use a balancing method called Synthetic Minority Oversampling Technique, or SMOTE, to generate additional examples of outliers and improve the anomaly detection model.|Tags:outlier, SMOTE, anomaly, DBSCAN|Course language: Python|Target Audience:Professionals with some Python experience who would like to expand their skills to learn about various outlier detection techniques|Prerequisite course required: Intro to Clustering</code> | |
|
|
| <code>Foundations of Python. This course introduces learners to the fundamentals of the Python programming language. Python is one of the most widely used computer languages in the world, helpful for building web-based applications, performing data analysis, and automating tasks. By the end of this course, learners will identify how data scientists use Python, distinguish among basic data types and data structures, and perform simple arithmetic and variable-related tasks.. tags: functions, basics, data-structures, control-flow. Languages: Course language: Python. Prerequisites: Prerequisite course required: Version Control with Git. Target audience: This is an introductory level course for data scientists who want to learn basics of Python and implement different data manipulation techniques using popular data wrangling Python libraries..</code> | <code>Course Name:Foundations of Python|Course Description:This course introduces learners to the fundamentals of the Python programming language. Python is one of the most widely used computer languages in the world, helpful for building web-based applications, performing data analysis, and automating tasks. By the end of this course, learners will identify how data scientists use Python, distinguish among basic data types and data structures, and perform simple arithmetic and variable-related tasks.|Tags:functions, basics, data-structures, control-flow|Course language: Python|Target Audience:This is an introductory level course for data scientists who want to learn basics of Python and implement different data manipulation techniques using popular data wrangling Python libraries.|Prerequisite course required: Version Control with Git</code> | |
|
|
| <code>Text Generation with LLMs in Python. This course provides a practical introduction to the latest advancements in generative AI with a focus on text. To start, the course explores the use of reinforcement learning in natural language processing (NLP). Learners will delve into approaches for conversational and question-answering (QA) tasks, highlighting the capabilities, limitations, and use cases of models available in the Hugging Face library, such as Dolly v2. Finally, learners will gain hands-on experience in creating their own chatbot by using the concepts of Retrieval Augmented Generation (RAG) in LlamaIndex.. tags: course, provides, practical, introduction, latest, advancements, generative, AI, focus, text., start,, course, explores, use, reinforcement, learning, natural, language, processing, (NLP)., Learners, will, delve, into, approaches, conversational, question-answering, (QA), tasks,, highlighting, capabilities,, limitations,, use, cases, models, available, Hugging, Face, library,, such, as, Dolly, v2., Finally,, learners, will, gain, hands-on, experience, creating, their, own, chatbot, using, concepts, Retrieval, Augmented, Generation, (RAG), LlamaIndex.. Languages: Course language: None. Prerequisites: No prerequisite course required. Target audience: No target audience.</code> | <code>Course Name:Text Generation with LLMs in Python|Course Description:This course provides a practical introduction to the latest advancements in generative AI with a focus on text. To start, the course explores the use of reinforcement learning in natural language processing (NLP). Learners will delve into approaches for conversational and question-answering (QA) tasks, highlighting the capabilities, limitations, and use cases of models available in the Hugging Face library, such as Dolly v2. Finally, learners will gain hands-on experience in creating their own chatbot by using the concepts of Retrieval Augmented Generation (RAG) in LlamaIndex.|Tags:course, provides, practical, introduction, latest, advancements, generative, AI, focus, text., start,, course, explores, use, reinforcement, learning, natural, language, processing, (NLP)., Learners, will, delve, into, approaches, conversational, question-answering, (QA), tasks,, highlighting, capabilities,, limitations,, use, cases, models, available, Hugging, Face, library,, such, as, Dolly, v2., Finally,, learners, will, gain, hands-on, experience, creating, their, own, chatbot, using, concepts, Retrieval, Augmented, Generation, (RAG), LlamaIndex.|Course language: None|Target Audience:No target audience|No prerequisite course required</code> | |
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"scale": 20.0, |
|
|
"similarity_fct": "cos_sim" |
|
|
} |
|
|
``` |
|
|
|
|
|
### Evaluation Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
|
|
|
* Size: 12 evaluation samples |
|
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
|
* Approximate statistics based on the first 12 samples: |
|
|
| | anchor | positive | |
|
|
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 46 tokens</li><li>mean: 162.92 tokens</li><li>max: 363 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 160.92 tokens</li><li>max: 361 tokens</li></ul> | |
|
|
* Samples: |
|
|
| anchor | positive | |
|
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>Fundamentals of Deep Learning for Multi GPUs. Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.. tags: multiple GPUs, neural networks, TensorFlow, parallelize. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals want to train deep neural networks on multi-GPU technology to shorten\nthe training time required for data-intensive applications.</code> | <code>Course Name:Fundamentals of Deep Learning for Multi GPUs|Course Description:Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.|Tags:multiple GPUs, neural networks, TensorFlow, parallelize|Course language: Python|Target Audience:Professionals want to train deep neural networks on multi-GPU technology to shorten\nthe training time required for data-intensive applications|No prerequisite course required</code> | |
|
|
| <code>Building Transformer-Based NLP Applications (NVIDIA). Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks. In this course, you'll construct a Transformer neural network in PyTorch, Build a named-entity recognition (NER) application with BERT, Deploy the NER application with ONNX and TensorRT to a Triton inference server. Upon completion, you’ll be proficient i.n task-agnostic applications of Transformer-based models. Data Society's instructors are certified by NVIDIA’s Deep Learning Institute to teach this course.. tags: named-entity recognition, text, Natural language processing, classification, NLP, NER. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals with basic knowledge of neural networks and want to expand their knowledge in the world of Natural langauge processing.</code> | <code>Course Name:Building Transformer-Based NLP Applications (NVIDIA)|Course Description:Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks. In this course, you'll construct a Transformer neural network in PyTorch, Build a named-entity recognition (NER) application with BERT, Deploy the NER application with ONNX and TensorRT to a Triton inference server. Upon completion, you’ll be proficient i.n task-agnostic applications of Transformer-based models. Data Society's instructors are certified by NVIDIA’s Deep Learning Institute to teach this course.|Tags:named-entity recognition, text, Natural language processing, classification, NLP, NER|Course language: Python|Target Audience:Professionals with basic knowledge of neural networks and want to expand their knowledge in the world of Natural langauge processing|No prerequisite course required</code> | |
|
|
| <code>Nonlinear Regression in Python. In this course, learners will practice implementing a variety of nonlinear regression techniques in Python to model complex relationships beyond simple linear patterns. They will learn to interpret key transformations, including logarithmic (log-log, log-linear) and polynomial models, and identify interaction effects between predictor variables. Through hands-on exercises, they will also develop practical skills in selecting, fitting, and validating the most appropriate nonlinear model for their data.. tags: nonlinear, regression. Languages: Course language: Python. Prerequisites: Prerequisite course required: Multiple Linear Regression. Target audience: This is an intermediate level course for data scientists who want to learn to understand and estimate relationships between a set of independent variables and a continuous dependent variable..</code> | <code>Course Name:Nonlinear Regression in Python|Course Description:In this course, learners will practice implementing a variety of nonlinear regression techniques in Python to model complex relationships beyond simple linear patterns. They will learn to interpret key transformations, including logarithmic (log-log, log-linear) and polynomial models, and identify interaction effects between predictor variables. Through hands-on exercises, they will also develop practical skills in selecting, fitting, and validating the most appropriate nonlinear model for their data.|Tags:nonlinear, regression|Course language: Python|Target Audience:This is an intermediate level course for data scientists who want to learn to understand and estimate relationships between a set of independent variables and a continuous dependent variable.|Prerequisite course required: Multiple Linear Regression</code> | |
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 3e-06 |
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- `max_steps`: 24 |
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- `warmup_ratio`: 0.1 |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 3e-06 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3.0 |
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- `max_steps`: 24 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: False |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: False |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | loss | |
|
|
|:------:|:----:|:-------------:|:------:| |
|
|
| 6.6667 | 20 | 0.046 | 0.0188 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.9.13 |
|
|
- Sentence Transformers: 3.1.1 |
|
|
- Transformers: 4.45.1 |
|
|
- PyTorch: 2.2.2 |
|
|
- Accelerate: 0.34.2 |
|
|
- Datasets: 3.0.0 |
|
|
- Tokenizers: 0.20.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
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